Stata Moderation Margins

/stata-moderation-margins — Zelner Moderation Marginal Effect Plots

Produces publication-quality marginal effect plots for binary logit/probit moderation hypotheses (the Zelner 2010 approach). Each panel shows how the marginal effect of the focal variable on Pr(y=1) changes continuously across a moderator's empirical range. Vertical bars = 95% CI (rspike style). Panels combined into one PNG.

When to use

• You have moderation hypotheses (H2, H3, H4…) tested with xtlogit / logit / probit
• You need a figure for the main text or appendix showing the moderation graphically
• The DV is binary (dummy). For count DVs, just show IRR coefficients — Zelner is not appropriate.

When NOT to use

• NBreg / Poisson / count outcomes → use IRR bar plots instead
• Simple linear moderation → OLS interaction plot is sufficient
• You only want one moderator → still works, just produces 1 panel

Inputs you must provide

1. Checkpoint path: path to the analysis .dta file (post data-construction)
2. Model spec per panel: each moderation hypothesis needs its own model. Provide:
   • Focal variable name (e.g. c_norm_eigen)
   • Moderator variable name (e.g. c_size)
   • Interaction term notation (e.g. c.c_norm_eigen##c.c_size)
   • Controls list
   • Moderator empirical range (from summarize: min, max, step size for ~60 points)
3. Output path: full path for the PNG

Stata template (adapt per project)

* zelner_moderation.do
use "PATH/TO/CHECKPOINT.dta", clear
set scheme s1mono

* ── Panel A: [Moderator 1 label] ─────────────────────────────────────────────
quietly xtlogit DEPVAR c.FOCAL##c.MODERATOR1 ///
    CONTROLS ///
    i.year i.industry_fe, re vce(robust)

* ~60 evenly-spaced values across [min, max] of moderator
margins, dydx(FOCAL) at(MODERATOR1 = (MIN(STEP)MAX)) predict(pu0)

marginsplot, ///
    title("(A) [Panel Title]", size(medsmall) margin(b=1)) ///
    xtitle("[Moderator label] (mean-centered)", size(small)) ///
    ytitle("Change in Pr(Divestiture)", size(small)) ///
    recastci(rspike) ///
    plot1opts(lcolor(black) lwidth(thin) msymbol(none)) ///
    ci1opts(lcolor(black) lwidth(vthin)) ///
    yline(0, lpattern(dash) lcolor(gs10)) ///
    graphregion(color(white)) plotregion(margin(sides)) ///
    legend(off) ///
    name(pA, replace)

* [Repeat for pB, pC, etc.]

* ── Combine panels ────────────────────────────────────────────────────────────
graph combine pA pB pC, ///
    cols(3) rows(1) ycommon ///
    graphregion(color(white)) ///
    xsize(10) ysize(3.5)

graph export "OUTPUT_PATH.png", replace width(2400) height(840)
di "Saved: OUTPUT_PATH.png"

Key details that always trip you up

• Use predict(pu0) not predict(pr) for xtlogit RE — this gives population-averaged Pr at u_i=0
• Use c.VAR##c.VAR notation (not i.) for continuous-by-continuous interactions
• ycommon is essential for comparing magnitudes across panels
• Step size = (max - min) / 60 → round to 3 significant figures
• Always mean-center moderators BEFORE computing the range — center is at 0

Output note

The figure caption should say: "Each panel plots the marginal effect of [focal] on Pr([DV]) evaluated at approximately 60 evenly spaced values of the moderator across its empirical range. Vertical bars = 95% confidence intervals."

Stata executable (Mac)

Always use: /Applications/StataNow/StataMP.app/Contents/MacOS/stata-mp Never use Stata 17 (license expired).